CN115833894A - Digital analog synthesis self-adaptive anti-interference method based on subarray - Google Patents
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Abstract
The invention discloses a digital analog synthesis self-adaptive anti-interference method based on a subarray, which comprises the following steps: 1) Dividing the non-overlapping non-uniformity of the N-element linear array into L sub-arrays; 2) Sampling a received signal, and performing spatial spectrum estimation to obtain the number of spatial interference and a guide vector thereof; 3) Detecting and judging the number of the space interference; 4) When the number of the plurality of disturbances is more than half of the number of the sub-arrays, an LCMV algorithm is adopted to obtain array element level simulation weight vectors, and then array element level phase shifters and attenuators are configured; 5) If the number of the interference is less than or equal to half of the number of the subarrays, utilizing a Capon beam forming algorithm to obtain a subarray-level digital weight vector, and further configuring a subarray-level digital phase shifter and an attenuator; 6) And outputting the synthesized beam. The method provided by the invention has the advantages of high angle measurement precision and strong anti-interference capability, and can reduce the consumption of hardware resources and improve the convergence rate of calculation.
Description
Technical Field
The invention relates to the technical field of array signal processing, in particular to a digital analog synthesis self-adaptive anti-interference method based on a subarray.
Background
Array antennas are often referred to as "phased arrays," an acronym for phased arrays. As the name implies, a phased array antenna is an array in which a number of radiating elements are arranged, and the feeding phase of each element is flexibly controlled by a computer. The array antenna is generally formed by arranging a plurality of omnidirectional antennas which are arranged according to a certain rule, and the beam pointing of an array antenna directional pattern can be controlled by changing the amplitude and phase relation of each antenna unit in the array.
In an electronic warfare environment, a radar system is inevitably subjected to various passive and active electromagnetic interferences, and interference signals enter a receiving system through a side lobe or a main lobe of a directional diagram of a receiving antenna, so that the signal detection capability of a receiver is greatly reduced. The anti-interference performance of the radar becomes an important index for measuring the performance of the radar, and the array antenna technology is an important means for improving the anti-interference capability of the radar. The Adaptive Digital Beam Forming (ADBF) technology of the array antenna enables the radar to automatically adopt a corresponding anti-interference scheme according to the interference characteristics, and can dynamically adjust working parameters along with the change of the anti-interference scheme, thereby achieving certain optimal performance. The ADBF technology of the array antenna has become an important content of the radar signal processing research at present.
The applications of the various beam forming methods proposed in recent years are mostly based on the array element level, which is feasible for small arrays with only a few antenna elements. However, the array antennas are developed toward large and sparse arrays, and particularly in the phased array radar, several hundreds or even thousands of antenna elements are frequently used. If the beamforming method is applied at the array element level in such large arrays, the received signal of each antenna element has to be processed separately, i.e. each element constitutes a receive channel, each receive channel has to include several amplification, mixing, and finally video processing or analog-to-digital (a/D) conversion. It is conceivable that the hardware cost will be multiplied. The dimensionality of the signal processor must be reduced while maintaining as good control of the array response as possible.
A common approach to reducing the dimensionality of signal processors is to employ adaptive array signal processing based on sub-arrays.
For an N-ary adaptive array, N degrees of freedom are available, wherein a part of the degrees of freedom are used to satisfy specific constraints, so as to utilize some available a priori information, which is called constrained degrees of freedom; the remaining degrees of freedom are used to adaptively suppress interference and noise, referred to as adaptive degrees of freedom. The more the adaptive freedom degree is, the larger the calculated amount of the adaptive algorithm is, and the slower the convergence speed is; otherwise, the convergence rate is increased. The adaptive array signal processing based on the subarray just reduces the adaptive freedom of the system to accelerate the convergence rate, so that the defect is that the capability of adaptively suppressing the interference is reduced.
Disclosure of Invention
The invention aims to provide a digital analog synthesis self-adaptive anti-interference method based on a subarray.
The technical solution for realizing the purpose of the invention is as follows: a digital analog synthesis self-adaptive anti-interference method based on a subarray comprises the following steps:
the method comprises the following steps: establishing N element linear array antennas, dividing the N element linear array antennas into L sub-arrays by adopting non-overlapping non-uniform division, and setting a dimension reduction matrix from array elements to the sub-arrays as T; the linear array receives an incident signal, wherein the incident signal consists of P mutually uncorrelated interference signals and noise in space; and N, L, P is a positive integer,
；
step two: sampling a subarray received signal, estimating a subarray level covariance matrix under a set snapshot number, performing characteristic decomposition on the subarray level covariance matrix to obtain an interference subspace and a noise subspace after decomposition, and performing spatial spectrum estimation on the result by adopting an MUSIC algorithm to obtain the number M of spatial interference and a spatial interference guide vector;
step three: judging the number M of the space interference obtained in the step two, if so, judging whether the space interference exists
If yes, executing the step four; if it is
If yes, executing the step five;
step four: constructing a constraint matrix C and a constraint response vector f by the expected signal steering vector and the spatial interference steering vector in the second step, calculating to obtain an array element level simulation weight vector by adopting an LCMV beam forming algorithm, configuring a phase shifter and an attenuator of each antenna array element according to the array element level simulation weight vector, and returning to the second step;
step five: calculating to obtain a sub-array level digital weight vector at a sub-array level by adopting a Capon beam forming algorithm, and configuring a phase shifter and an attenuator of each sub-array channel according to the sub-array level digital weight vector;
step six: and synthesizing the signals of each subarray channel in the step five into a path of wave beam for output.
Further, in the step one, the non-overlapping submatrices are used for dividing the dimension reduction matrix T:
further, in the second step, the following method is adopted to calculate the covariance matrix of the subarray level and obtain the interference subspace and the noise subspace:
the sub-array stage receives samples at time k as
Is a positive integer and is a non-zero integer,
for the snapshot number, i.e., the number of samples, the estimation of the subarray level covariance matrix is:
to pair
And decomposing the characteristic value to obtain:
wherein 
Is composed of
Is determined by the characteristic value of (a),
as a characteristic value
A corresponding feature vector, wherein:
Further, in the second step, the following method is adopted to obtain the space interference guide vector:
obtaining spatial spectrum function by MUSIC algorithm
By
Angle corresponding to the peak value of
Obtaining a spatial interference steering vector
。
Further, in the fourth step, a constraint matrix C is constructed by the desired signal steering vector and the spatial interference steering vector, and a constraint response vector f is:
wherein 
。
Further, in the third step, an LCMV algorithm is used to obtain array element-level simulation weight vectors
The method comprises the following steps:
to find out
。
Further, in the fifth step, a method for obtaining the digital weight vector of the subarray level by using a Capon beam forming algorithm at the subarray level is as follows:
the solution of the following optimization problem is taken as the sub-array level digital weight vector
：
to obtain
。
Further, the synthesized beam output expression obtained in the sixth step is as follows:
compared with the prior art, the method has the following remarkable advantages: (1) The number of interference signals is circularly detected and judged at the subarray level, and array element level simulation weight vectors are configured, so that the number of interference input to the subarray level can be gradually reduced, and finally the number of interference suppression is equivalent to the number of fully adaptive array processing, and more interference can be processed compared with a conventional subarray-based beam forming method; (2) Compared with the conventional full-adaptive array processing beam forming method, the method can greatly reduce the consumption of hardware resources, improve the calculation convergence rate and is suitable for the phased array radar with thousands of array element antennas. (3) The method combines the advantages of analog beam forming at an array element level and digital beam forming at a subarray level, so that the system has stronger anti-interference capability and higher angle measurement precision compared with a common adaptive beam forming mode based on the subarray.
Drawings
Fig. 1 is a signal processing flow chart according to an embodiment of the present invention.
Fig. 2 is the antenna pattern simulation results using conventional beamforming at the array element level and LCMV algorithm and Capon algorithm, respectively, at the subarray level.
Fig. 3 shows simulation results of antenna patterns of a full array using LCMV algorithm.
Fig. 4 shows simulation results of antenna patterns using LCMV algorithm and conventional beamforming at the array element level, respectively, and Capon algorithm at the sub-array level.
Detailed Description
The method of the invention is further described below with reference to the figures and examples.
The basic scheme of the invention comprises the following six steps, and the specific signal processing flow is shown in figure 1.
The method comprises the following steps: establishing N element linear array antennas, dividing the N element linear array antennas into L sub-arrays by adopting non-overlapping non-uniform division, and setting a dimension reduction matrix from array elements to the sub-arrays as T; the linear array receives incident signals which are uncorrelated with each other by P in spaceInterference signal and noise components; and N, L, P is a positive integer,
；
step two: sampling a subarray received signal, estimating a subarray level covariance matrix under a set snapshot number, performing characteristic decomposition on the subarray level covariance matrix to obtain an interference subspace and a noise subspace after decomposition, and performing spatial spectrum estimation on the result by adopting an MUSIC algorithm to obtain the number M of spatial interference and a spatial interference guide vector;
step three: judging the number M of the space interference obtained in the step two, if so, judging
If yes, executing the step four; if it is
If yes, executing the step five;
step four: constructing a constraint matrix C and a constraint response vector f by the expected signal steering vector and the spatial interference steering vector in the second step, calculating to obtain an array element level simulation weight vector by adopting an LCMV beam forming algorithm, configuring a phase shifter and an attenuator of each antenna array element according to the array element level simulation weight vector, and returning to the second step;
step five: calculating to obtain a sub-array level digital weight vector at a sub-array level by adopting a Capon beam forming algorithm, and configuring a phase shifter and an attenuator of each sub-array channel according to the sub-array level digital weight vector;
step six: and synthesizing the signals of each subarray channel in the step five into a path of wave beam for output.
Based on the basic scheme, the model for dividing the subarray in the first step is as follows:
firstly, a signal model is established, a linear array is provided, the number of array elements is N, the distances between the array elements are d, the linear array is divided into L sub-arrays, and L receiving channels are formed. Now assume that there are P mutually uncorrelated interfering signals, the complex envelope of which is
The steering vector matrix of the interference signal is
Background noise of
Then array element receives signal
。
Subarray division based on dimension reduction matrix
, wherein 
In order to weight the coefficient diagonal matrix,
is composed of
The subarrays form a matrix, in all elements of the 1 st column, the element value corresponding to the array element serial number of the ith subarray is 1, the rest are 0, wherein L is all positive integers between 1 and L, and then the received signals of the subarrays are 1
。
If the signal is sampled at the time k, the result is
Respectively, the sub-array level, the samples received by the array element level at the time k satisfy
And a subarray level covariance matrix
And array element level covariance matrix
The relationship of (c) is:
(ii) a Subarray level steering vector
And array element level steering vector
The relationship of (1) is:
wherein ：
based on the basic scheme, the method for calculating the subarray level covariance matrix and the eigen decomposition of the receiving array in the second step comprises the following steps:
the covariance matrix should be obtained theoretically, the statistical properties of the received signals need to be known accurately, but actually, the estimation of the covariance matrix can only be obtained by sampling at a certain number of snapshots K
。
Receiving signal to subarray
Sampling is carried out to obtain a sampling matrix of the subarray under K snapshots
If the covariance matrix of the subarray level sampling is:
. To pair
And decomposing the characteristic value to obtain:
wherein 
Sampling covariance matrices for subarray levels
Is determined by the characteristic value of (a),
as a characteristic value
Corresponding feature vector, wherein
Based on the basic scheme, the method for obtaining the number M of the spatial interferences and the steering vectors thereof by adopting the MUSIC algorithm in the step two comprises the following steps:
theoretically, the signal space and the noise space are orthogonal, so the column vector of the signal space direction matrix a and the feature vector of the noise space
Also orthogonal, and the column vector of a corresponds one-to-one to the direction of arrival of the signal, and by this property, the eigenvectors of the noise space can be usedTo solve for the direction of arrival of the signal. Firstly, a noise matrix is constructed, namely, the noise subspace obtained in the step two
Then defining a spatial spectrum function
：
As can be seen from the definition of the spatial spectrum function, when
And
when the two-dimensional images are orthogonal to each other,
in (1)
From
Go through, thus
A spectrum peak is generated, and the spectrum peak corresponds to
The value is an estimate of the direction of arrival of the signal, so the direction of arrival of the signal can be estimated by searching for the peak of the spatial spectral function.
By
Angle corresponding to the peak value of
To obtain the guide vector of the space interference
。
Based on the basic scheme, after the second step is carried out, the estimated guide vector of the spatial interference is obtained, and in the fourth step, in order to obtain the array element level simulation weight vector
It is necessary to construct constraint matrix and constraint response vector to make the algorithm self-adaptively suppress the interference signal and keep the beam pointing
The gain of the LCMV algorithm is constant, and the flow of the LCMV algorithm constructed by the embodiment is as follows:
by a constraint matrix
；
Constrained response vector
，
wherein 
；
Obtaining an algorithm model
；
To obtain
。
Simulating weight vectors according to the array element level
Configuring a phase shifter and an attenuator of each antenna array element; thus, although the above steps are in subarraysThe sampling estimation is carried out by stages, but the configuration of the array element stage can bring about a plurality of advantages, such as the advantages of less equipment quantity and less calculation quantity based on the subarray mode, and the freedom degree of the system can be improved, namely the problem of insufficient freedom degree of interference suppression caused by the subarray division can be improved. The method can inhibit interference of subarray number level through one cycle, and finally can inhibit interference of subarray number level through array element level simulation weight vector obtained through calculation after a plurality of cycles, thereby overcoming the defect that available freedom degree of other adaptive array signal processing methods based on subarray is reduced.
Based on the basic scheme, in the fifth step, a Capon beam forming algorithm is adopted at the subarray level to obtain a subarray level digital weight vector
The method comprises the following steps:
the solution of the following optimization problem is taken as the sub-array level digital weight vector
To obtain
。
Thus, when the number of space interference is excessive
Array element level analog weight vectors are configured through a plurality of cycles, most interference is restrained, and interference estimated by sub-array sampling in the last cycle is little
Interference suppression can be performed at the subarray level, thereby combining the advantages of analog beam forming at the array element level and digital beam forming at the subarray level, and enabling the system to be compared with a common base stationThe adaptive beam forming mode of the subarray has stronger anti-interference capability and higher angle measurement precision.
And (3) simulating an array directional diagram, wherein the simulation adopts a uniform linear array, and simulation parameters are shown in table 1.
Table 1 simulation parameter settings
Fig. 2 is an antenna pattern simulation result using conventional beamforming at the array element level, and using LCMV algorithm and Capon algorithm, respectively, at the subarray level;
FIG. 3 shows simulation results of antenna patterns for a full array using LCMV algorithm;
fig. 4 shows simulation results of antenna patterns using LCMV algorithm and conventional beamforming at the array element level, respectively, and Capon algorithm at the sub-array level.
The LCMV algorithm and the Capon algorithm have respective advantages as can be seen from the figure 2, and the LCMV algorithm has better interference suppression effect due to finer constraint conditions as can be seen from a simulation result diagram; the system designed by the invention uses the LCMV algorithm at the array element level to obtain the array element level analog weight vector, most of interference can be accurately suppressed at the analog part, and the Capon algorithm is adopted at the subarray level to obtain the subarray level digital weight vector, which is equivalent to suppressing the residual interference by matching with the analog part, thereby having stronger interference suppression capability.
As can be seen from fig. 3, although the full-array LCMV beamforming method has a strong anti-interference capability, the implementation based on the full-array method generally causes the disadvantages of large equipment amount, high cost, and the like.
Fig. 4 shows that compared with a general adaptive beam forming method based on a subarray, such as array element CBF + subarray Capon, there is no suppression capability for interference in the direction of 20 °, and the digital analog synthesis adaptive anti-interference method based on a subarray of the system obviously has a stronger anti-interference capability.
Claims (8)
1. A digital analog synthesis self-adaptive anti-interference method based on a subarray is characterized by comprising the following steps:
the method comprises the following steps: establishing N element linear array antennas, dividing the N element linear array antennas into L sub-arrays by adopting non-overlapping non-uniform division, and setting a dimension reduction matrix from an array element to the sub-arrays as T; the linear array receives an incident signal, wherein the incident signal consists of P mutually uncorrelated interference signals and noise in space; and N, L, P is a positive integer,
；
step two: sampling a subarray received signal, estimating a subarray level covariance matrix under a set snapshot number, performing characteristic decomposition on the subarray level covariance matrix to obtain an interference subspace and a noise subspace after decomposition, and performing spatial spectrum estimation on the result by adopting an MUSIC algorithm to obtain the number M of spatial interference and a spatial interference guide vector;
step three: judging the number M of the space interference obtained in the step two, if so, judging whether the space interference exists
If yes, executing the step four; if it is
If yes, executing the step five;
step four: constructing a constraint matrix C and a constraint response vector f by the expected signal steering vector and the spatial interference steering vector in the second step, calculating to obtain an array element level simulation weight vector by adopting an LCMV beam forming algorithm, configuring a phase shifter and an attenuator of each antenna array element according to the array element level simulation weight vector, and returning to the second step;
step five: calculating to obtain a sub-array level digital weight vector at a sub-array level by adopting a Capon beam forming algorithm, and configuring a phase shifter and an attenuator of each sub-array channel according to the sub-array level digital weight vector;
step six: and synthesizing the signals of each subarray channel in the step five into a path of wave beam for output.
2. The subarray-based digital-to-analog synthesis adaptive interference rejection method according to claim 1, wherein the dimension reduction matrix T in the first step satisfies:
3. the subarray based digital-analog synthesis adaptive anti-interference method according to claim 1, wherein the second step estimates the subarray level covariance matrix and obtains an interference subspace and a noise subspace by using the following method:
the sub-array stage receives samples at time k as
Is a positive integer and is a non-zero integer,
the number of snapshots is the sampling number; estimation of sub-array level covariance matrix
Comprises the following steps:
to pair
And decomposing the characteristic value to obtain:
wherein 
Is composed of
Is determined by the characteristic value of (a),
，
as a characteristic value
Corresponding feature vector, wherein
4. The subarray-based digital-analog synthesis adaptive anti-interference method according to claim 1, wherein the number M of the spatial interferences and the spatial interference steering vector are obtained in the second step by using the following method:
obtaining spatial spectrum function by MUSIC algorithm
, wherein 
A vector is steered at the level of the sub-array of signals,
is an estimated noise subspace;
by
Angle corresponding to the peak value of
To obtain a spatial interference steering vector
And number of
。
5. The subarray based digital-to-analog synthesis adaptive interference rejection method according to claim 1, wherein in step four, a constraint matrix C is constructed from the desired signal steering vector and the spatial interference steering vector, and a constraint response vector f is:
wherein 
And array element level steering vectors.
6. The adaptive anti-jamming method based on digital-analog synthesis of subarray of claim 1, wherein in step four, array element-level analog weight vector is obtained by LCMV algorithm
The method comprises the following steps:
wherein ,
is an array element level covariance matrix,
is a constraint matrix that is a function of,
is a vector of the response of the constraint,
means taking conjugate transpose to obtain
。
7. The subarray-based digital-analog synthesis adaptive anti-interference method according to claim 1, wherein in the fifth step, a method for obtaining the subarray-level digital weight vector by using a Capon beamforming algorithm at the subarray level comprises:
the solution of the following optimization problem is taken as a sub-array level digital weight vector
：
8. The adaptive anti-jamming method based on subarray digital analog synthesis according to claim 1, wherein the synthesized beam output expression obtained in the sixth step is as follows:
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| CN114563764A (en) * | 2021-12-03 | 2022-05-31 | 北京无线电测量研究所 | Self-adaptive beam generation method and system |
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